The Tool Desk
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To connect an LLM to an app safely, call the model from your server, decide exactly what data leaves your system, and treat both prompts and model responses as untrusted. Keep credentials out of client code, restrict any tools the model can invoke, validate consequential actions in your application, and monitor the feature after launch. An SDK call is only one part of the integration.
Put the model behind a server-side application boundary
A browser or mobile app should send a request to your backend; the backend should apply your app’s authentication, authorization, and input rules before calling the model provider. This keeps privileged provider credentials out of code that ships to users and gives your application a place to enforce limits and record operational signals.
- Create a server-side credential. Store the provider key in a server-side environment variable or a managed secret store. Do not embed it in browser JavaScript, a mobile binary, or a public repository.
- Use the provider’s SDK or API from the backend. OpenAI’s Developer quickstart demonstrates a provider-specific pattern: create an API key, set it as an environment variable, install the SDK for the runtime, and make an API request. Follow the current quickstart for the specific provider and runtime you use.
- Put app checks before the request. Authenticate the user, check whether they may use the feature, enforce request-size and usage limits, and send only the context the task requires.
- Keep control of the response. Validate the model’s response in your backend before returning it to a client or using it to trigger another operation.
This design does not make a model call inherently safe. It establishes a boundary where your application can make decisions the model should not make for itself.
Map the data before it leaves your system
Trace the full path of information, not just the text in the initial prompt. A feature may send user input, retrieved documents, system instructions, conversation history, tool arguments, and other application context; it may also store responses or write request details to your own logs.
#1 Best Overall
| Data to account for | Questions to answer |
|---|---|
| User input and uploaded content | Does the model need the full content? Can you remove identifiers or redact sensitive fields first? |
| Retrieved material and conversation history | Which sources are included, who can access them, and how long is the history kept? |
| Prompts, model responses, and tool arguments | Can these contain personal, confidential, or security-sensitive information? Who can inspect them in provider and application logs? |
| Persisted application state and your logs | What is stored, where is it stored, who can access it, and when is it deleted? |
Use the answers to minimize what you send, set access controls, choose retention periods for your own systems, and explain the relevant handling to users. A provider’s settings do not describe every data flow in your application or through other services you use.
Separate training use from retention
OpenAI’s data-controls documentation says API data is not used to train or improve its models unless the customer opts in. That statement is not the same as saying the data is never retained: OpenAI says default abuse-monitoring logs may contain prompts and responses and may be retained for up to 30 days. Some features separately persist application state. OpenAI says Zero Data Retention and Modified Abuse Monitoring controls require approval, and some features may still store state. Check the current conditions for the exact endpoint and features you use before making a privacy claim. Other providers may have different terms and controls.
Threat-model prompts and model responses as untrusted
OWASP’s 2025 Top 10 for Large Language Model Applications includes prompt injection, insecure output handling, denial of service, sensitive information disclosure, insecure plugin design, excessive agency, and overreliance. These are risks to assess in your own feature, not proof that every LLM app has each vulnerability.
Content supplied by users or retrieved from documents can contain instructions designed to steer the model. A system prompt may guide behavior, but it is not an authorization boundary. Enforce access and business rules in application code, independently of what the model says it should do.
Likewise, generated text or structured values may be wrong, malformed, or unsafe to use in another system. Do not pass model-generated content directly into SQL, a shell command, HTML, or a privileged workflow. Validate or safely encode it for the specific destination, and use established application interfaces rather than treating generated text as executable instructions.
Give tools narrow permissions and explicit checks
If the model can call tools—such as searching records, sending messages, or changing account data—the consequences depend on what those tools are allowed to do. OWASP identifies excessive agency and insecure plugin design as risks; the following controls are engineering choices to reduce them.
- Expose only the particular operation needed for the task, rather than a general-purpose database, shell, or administrative interface.
- Enforce the signed-in user’s identity, permissions, ownership, and business rules inside each tool implementation. Do not let a model-supplied user ID or claim of authorization stand in for a verified check.
- Validate every argument in ordinary application code, including identifiers, ranges, and allowed operation types.
- For consequential actions, require a human confirmation or make the operation reversible where practical.
A structured response format can make tool arguments easier to parse, but a successful parse does not prove the requested action is permitted. Your application remains responsible for that decision.
Limit abuse, resource use, and failure impact
OWASP’s denial-of-service risk discussion notes that resource-heavy model operations can disrupt service and increase costs. Set limits appropriate to your traffic and threat model: cap input and output sizes, budget requests, apply rate limits, and set timeouts. Design a graceful failure path so a provider error or limit does not leave a user-facing workflow in an unsafe or confusing state.
Best Value
Also decide what happens when the model is unavailable, refuses a request, returns unusable output, or takes too long. Depending on the feature, the safe response may be to ask the user to retry, offer a non-model fallback, or stop the operation without making changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate behavior and monitor the production feature
Before launch, build test cases around the feature’s expected uses, edge cases, and abuse attempts. Check not only whether the model produces a plausible answer but whether the application protects data, rejects unauthorized actions, handles malformed outputs, and fails safely.
OpenAI’s API reference identifies request IDs and request- and token-rate-limit headers, recommends logging request IDs in production, and notes that prompting behavior can change between model snapshots. It recommends pinned model versions and evals for more consistent behavior. Apply the equivalent observability guidance for the provider you use, and avoid putting sensitive prompt contents into logs unless you have a justified need and appropriate controls.
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Use moderation for its specific job
OpenAI’s Moderations API reference describes an endpoint that classifies text and/or image inputs for potentially harmful content. A moderation check can support a safety policy for a particular feature, but it does not establish that a user is authorized, stop prompt injection, or make a tool call safe. Treat it as one control in a broader design, not a substitute for access checks, validation, or monitoring.
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